A review of deep learning-based image style transfer research

Abstract

ABSTRACT Image style transfer is a technique that applies artistic styles to natural images while maintaining their original content structures. This technology has a broad spectrum of applications in the stylization of images, comics, and videos, and has emerged as a significant area of research within the field of artificial intelligence. This paper seeks to advance research on deep learning-based image style transfer by summarizing and discussing the principal methods and illustrative outcomes within this domain. It commences with a review of traditional image style transfer algorithms, analyzing their strengths and limitations. The paper then delves into deep learning-based style transfer algorithms, classifying them into image optimization-based and model optimization-based approaches based on the efficiency of transfer. Through an analysis of the current state of image style transfer research, the paper outlines the developmental trajectory and recent milestones in the field. It evaluates the merits and demerits of various methods, addressing current issues such as suboptimal image style transfer quality, model specificity, and the need for enhanced semantic depth. Via comparative analysis, the paper proposes enhancement strategies and suggests future research directions.